Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 2/28/2025 (with Apparent priority date of 12/22/2020). Claims 21-40 are pending of which claims 21, 28 and 34 are independent. As such, claims 21-40 have been examined.
This Application was published as US 20250131338.
This Application is a continuation of 17/130,869 issued as U.S. 12190207. Although both the parent application and instant application involve terms, definition and machine learning, the claims of the instant application appears to focus in scoring definition using multiple models and sending the scores to clients to gather feedback while the parent case is about training a model using specific CNN architecture and POS features. The claims in parent application and instant application appears to involve distinctly different processes. Therefore, double patenting rejection is not being applied at this time.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-26, 28-32, and 34-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 21 recites a system that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “at least one hardware processor”, “at least one non-transitory memory storing instructions,”, “a plurality of machine learning models”, and “a plurality of client devices”, nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 21 pertains to evaluating definition of a term, which is a mental process that a human can do. Its also noted that some of the steps also can involve human gathering activities, like getting feedback from other humans. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
obtain a plurality of terms and a plurality of definitions associated with the plurality of terms; (e.g., a human printing out a list of terms and their associated definition.)
derive a feature from a definition among the plurality of definitions, wherein the feature includes a part of speech; (e.g., mentally or using pen to label or identify whether words from definition is a noun, verb, or adjective.)
provide the feature to a plurality of machine learning models; (e.g., consulting other humans, handing out printed paper to other humans who may be expert in words. [machine learning model is generic computer components without any further specificity])
obtain from the plurality of machine learning models a plurality of scores associated with the definition, wherein a machine learning model among the plurality of machine learning models provides a score among the plurality of scores associated with the definition; (e.g., receiving scores from the various human experts on scoring of the definitions.)
send the plurality of terms, the plurality of definitions, and the plurality of scores to a plurality of client devices; (e.g., handing out paper with terms, definition, and their associated score from the human expert to a team of human reviewer.) [client device are generic computer components]
receive a plurality of indications from the plurality of client devices of whether the plurality of definitions satisfies one or more definition quality guidelines; (e.g., receiving feedback from the team of human reviewers on whether the definition is good enough.)
label each definition as satisfying each of the one or more definition quality guidelines or not based at least in part on the plurality of indications; (e.g., using paper and pen, label or noting if the definition pass or fail based on the guideline.)
and generate a set of labeled definitions based on the one or more definition quality guidelines. (e.g., using pen and paper, under new heading, approved/passed, write down the term and the associated definition once the definition for that term has been reviewed and approved by the panel of human judges or reviewers.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 21 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 21 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“at least one hardware processor”, “at least one non-transitory memory storing instructions,”, “a plurality of machine learning models”, and “a plurality of client devices”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 21 recites generic computer components (“at least one hardware processor”, “at least one non-transitory memory storing instructions,”, “a plurality of machine learning models”, and “a plurality of client devices”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 28 recites a method claim that corresponds (does not contain the deriving step) to the system of claim 21 and is therefore rejected under the same grounds as claim 21 above. Claim 28 is not patent eligible.
Claim 34 recites a computer-readable storage medium claim that corresponds (does not contain the deriving step) to the system of claim 21 and is therefore rejected under the same grounds as claim 21 above. While claim 34 further recites “non-transitory computer readable storage medium comprising instructions recorded there on”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 34 is not patent eligible.
Claims 22-26, 29-32, and 35-39 depend from independent claims 21, 28 and 34 respectively, do not remedy any of the deficiencies of claims 21, 28 and 34, and therefore are rejected on the same grounds as claim 21, 28 and 34 from above.
Claim 22 further comprising: wherein the plurality of indications are in a form of a binary affirmative or negative response. (e.g., pass or fail)
Claim 23 further recite: comprising instructions to label a definition as having satisfied a definition quality guideline only if at least three of indications among the plurality of indications for that definition quality guideline are affirmative. (e.g., determining if certain number of criteria or metric has been met.)
Claim 24 further comprising: wherein when the plurality of indications are associated with a scale, comprising instructions to convert the plurality of indications associated with the scale into a binary affirmative or negative response. (e.g., converting a scale into pass/fail response.)
Claim 25 further recites: wherein the plurality of indications received from the plurality of client devices are curated prior to labeling each definition by removing indications that do not match selected criteria. (e.g., sorting and filtering of feedback)
Claim 26 further recites: comprising instructions to: receive an overall score for a definition among the plurality of definitions; and derive a weight for each individual guideline based on the overall score. (e.g., collecting scores and determine which individual guideline is more important, more weights assigned to it.)
The analysis of Claims 29-32 corresponds to claims 23-26, and therefore similar rationale of rejection is applied to these claims respectively.
The analysis of Claims 35-39 corresponds to claim 22-26, and therefore similar rationale of rejection is applied to the claim.
In sum, claims 22-26, 29-32, and 35-39 depend from claims 21, 28 and 34 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 22-26, 29-32, and 35-39 amount to anything more than the same or a similar abstract idea as recited in claims 1. Nor do any limitations in claims 22-26, 29-32, and 35-39: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 22-26, 29-32, and 35-39 are not patent eligible.
Dependent claims 27, 33 and 40 recites a specific method of training of a neural network, application of supervised natural language processing training for definition generation, therefore it is patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-22, 28 and 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), and further in view of Lasser (US 20170351661).
Regarding Claim 21, O’Neill discloses: 21. (New) A system comprising: at least one hardware processor; ([0035] Digital data processor 505 is a system of one or more data processing devices that perform operations in accordance with one or more sets of machine-readable instructions.)
and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: ([0035] Digital data processor 505 is a system of one or more data processing devices that perform operations in accordance with one or more sets of machine-readable instructions.)
obtain a plurality of terms and a plurality of definitions associated with the plurality of terms; ([0057] The system performing process 1100 displays one or more definitions of the selected term in accordance with these rankings at 1125.)
derive a feature from a definition among the plurality of definitions, wherein the feature includes a part of speech; ([0071] In some implementations, user confirmation data collection 1500 also includes information characterizing the semantic and/or syntactic context of the term within the text. For example, user confirmation data collection 1500 can include a characterization of the part of speech of the term within the text and characteristics of other terms that were in the vicinity of the term in the text.)
obtain from the plurality of machine learning models a plurality of scores associated with the definition, wherein a machine learning model among the plurality of machine learning models provides a score among the plurality of scores associated with the definition; ([0073] Returning to FIG. 13, the system performing process 1300 ranks definitions based at least in part on the stored characterizations of user confirmation at 1320. For example, a statistical analysis of user confirmations of the propriety or impropriety of definitions can be used to determine parameters that represent the likelihood that different term definitions are appropriate for texts in different contexts. In some implementations, such a statistical analysis can be performed by a data processing system such as dictionary data system 1410 that aggregates user confirmations received from a variety of different devices and then distributes the results of the statistical analysis to those devices, e.g., for use in ranking different definitions of selected terms (e.g., at 1120 in process 1100 (FIG. 11)). Other machine-learning approaches, including neural-network-based approaches, can also rank definitions based at least in part on the stored characterizations of user confirmation.)Also see para 0075-0076 which discuss ranking.
send the plurality of terms, the plurality of definitions, and the plurality of scores to a plurality of client devices; ([0073] Returning to FIG. 13, the system performing process 1300 ranks definitions based at least in part on the stored characterizations of user confirmation at 1320. For example, a statistical analysis of user confirmations of the propriety or impropriety of definitions can be used to determine parameters that represent the likelihood that different term definitions are appropriate for texts in different contexts. In some implementations, such a statistical analysis can be performed by a data processing system such as dictionary data system 1410 that aggregates user confirmations received from a variety of different devices and then distributes the results of the statistical analysis to those devices, e.g., for use in ranking different definitions of selected terms (e.g., at 1120 in process 1100 (FIG. 11)).
receive a plurality of indications from the plurality of client devices of whether the plurality of definitions satisfies one or more definition quality guidelines; ([0011] A system can receive a user confirmation of the propriety or impropriety of a displayed definition.)
label each definition as satisfying each of the one or more definition quality guidelines or not based at least in part on the plurality of indications; ([0073] Returning to FIG. 13, the system performing process 1300 ranks definitions based at least in part on the stored characterizations of user confirmation at 1320. For example, a statistical analysis of user confirmations of the propriety or impropriety of definitions can be used to determine parameters that represent the likelihood that different term definitions are appropriate for texts in different contexts. In some implementations, such a statistical analysis can be performed by a data processing system such as dictionary data system 1410 that aggregates user confirmations received from a variety of different devices and then distributes the results of the statistical analysis to those devices, e.g., for use in ranking different definitions of selected terms (e.g., at 1120 in process 1100 (FIG. 11)). Other machine-learning approaches, including neural-network-based approaches, can also rank definitions based at least in part on the stored characterizations of user confirmation.) [Storing characterizations of user confirmations (propriety/impropriety) functionally operates as a label for determining if definitions are appropriate or satisfy quality standards.]
O’Neill does not appear to disclose the following: provide the feature to a plurality of machine learning models; and generate a set of labeled definitions based on the one or more definition quality guidelines.
Kowolenko in the related art discloses: provide the feature to a plurality of machine learning models; ([0040] the system utilizes the output from a rules-based system coupled with part of speech (POS) analysis to generate phrases that have the appropriate specificity and context for the domain under investigation. The dictionaries provide the specificity, use of POS improves context as placement of terms in noun-verb-noun relationships uses rules of grammar to improve the relevancy of the terms that are used as either positive or negative training data in the machine learning models.)
O’Neill and Kowolenko are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill to combine the teaching of Kowolenko, because human in the loop mechanism improves specificity and content accuracy (Kowolenko, [Abstract]).
O’Neill and Kowolenko do not appear to disclose: generate a set of labeled definitions based on the one or more definition quality guidelines.
Lasser in the related art discloses: generate a set of labeled definitions based on the one or more definition quality guidelines. ([0161] A “full meaning” of the segment is an understanding of the meaning of the text sufficient for generating a full and accurate translation of the segment of text into a second language. A “partial meaning” of the segment is an understanding of the meaning of the text sufficient for identifying at least one concept or topic appearing in the segment of text, even if the understanding is insufficient for generating a full translation of the segment of text into a second language.) [Full meaning means labeled as sufficient for full and accurate translation, and partial meaning is labeled as sufficient only for identifying concepts or topics]
O’Neill, Kowolenko and Lasser are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill and Kowolenko to combine the teaching of Lasser, because defining text content by full or partial meaning enable machine learning processing to determine if the text translation or definition is good enough (Lasser, [0161]).
Regarding Claim 22, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill further discloses: wherein the plurality of indications are in a form of a binary affirmative or negative response. ([0063] The system performing process 1300 receives a user confirmation of the propriety or impropriety of a displayed definition at 1310. The user confirmation can be received over an input element of the device on which the definition is displayed. The user confirmation can be received in any of a variety of different forms. For example, the user confirmation can be received as a binary response to a query inquiring as to the propriety of a definitions (e.g., "Was this definition appropriate?" or "Was this definition inappropriate?").)
Claim 28 recites a method claim that corresponds (does not contain the deriving step) to the system of claim 21 and is therefore rejected under the same grounds as claim 21 above. (this claim lacks the deriving step of claim 21)
Regarding Claim 34, O’neill discloses: 34. (New) A non-transitory, computer-readable storage medium comprising instructions recorded there on, wherein the instructions when executed by at least one data processor of a system, cause the system to: ([0082] Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus.)
As for the rest of the claim, they claim the elements corresponding to claim 21, therefore the rationale applied in rejection of claim 21 is equally applicable. (this claim lacks the deriving step of claim 21)
Regarding Claim 35, it is a computer readable storage medium claim that recite similar elements from claim 22, therefor the rationale applied in the rejection of claim 22 is also applicable.
Claims 23, 29 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), further in view of Lasser (US 20170351661), and furthermore in view of Aher (US 20220114339).
Regarding Claim 23, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill/Kowolenko/Lasser do not appear to disclose comprising instructions to label a definition as having satisfied a definition quality guideline only if at least three of indications among the plurality of indications for that definition quality guideline are affirmative.
Aher in the related art discloses: comprising instructions to label a definition as having satisfied a definition quality guideline only if at least three of indications among the plurality of indications for that definition quality guideline are affirmative. ([0059] In another example, the threshold is dynamic and requires that a percent of the responses reviewed answer the question and confirm the condition of the request is met. In this example, the identification application may have a required confirming response percentage of 59% and may find there were five responses to the question. In this example, the identification application may determine that three of the five responses confirm the condition of the request is met and may consider the threshold exceeded because 60% of the responsive posts answer the question and confirm the condition of the request is met. However, if more responses to the question do not confirm the condition is met before the buffer time expires, the identification application may not confirm the threshold is exceeded.)
O’Neill, Kowolenko, Lasser and Asher are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill/Kowolenko/Lasser to combine the teaching of Asher, because by enforcing a specific confirming percentage, the system ensures a majority of the users agrees before confirming the condition. This reduces the risk of acting on divided or uncertain results (Asher, [0059]).
Claims 29 and 36, are method and computer-readable storage medium claims that recite similar elements from claim 23, therefor the rationale applied in the rejection of claim 23 are also applicable.
Claims 24, 30 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), further in view of Lasser (US 20170351661), and furthermore in view of Bhattacharjya (US 20200160189).
Regarding Claim 24, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill/Kowolenko/Lasser do not appear to disclose wherein when the plurality of indications are associated with a scale, comprising instructions to convert the plurality of indications associated with the scale into a binary affirmative or negative response.
Bhattacharjya in the related art discloses: wherein when the plurality of indications are associated with a scale, comprising instructions to convert the plurality of indications associated with the scale into a binary affirmative or negative response. ([0092] using a linear regression model on the proposed cause-effect scores (after z-scoring). This strength is measured on a scale of −1 (strong no) to 1 (strong yes) and obtained by applying a positive (negative) sign for the binary response yes (no) and averaging over raters' confidences.)
O’Neill, Kowolenko, Lasser and Bhattacharjya are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill/Kowolenko/Lasser to combine the teaching of Bhattacharjya, because converting scale to binary response enhance simplicity and algorithm compatibility (Bhattacharjya, [0092]).
Claims 30 and 37, are method and computer-readable storage medium claims that recite similar elements from claim 24, therefor the rationale applied in the rejection of claim 24 are also applicable.
Claims 25, 31 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), further in view of Lasser (US 20170351661), and furthermore in view of Wu (US 20190236478).
Regarding Claim 25, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill/Kowolenko/Lasser do not appear to disclose wherein the plurality of indications received from the plurality of client devices are curated prior to labeling each definition by removing indications that do not match selected criteria.
Wu in the related art discloses: wherein the plurality of indications received from the plurality of client devices are curated prior to labeling each definition by removing indications that do not match selected criteria. ([0028] FIG. 2 shows an example of the IDLE system architecture 30. There are four main components in the data labeling framework: (1) a multi-level worker platform 32 that assigns tasks to domain experts 34 and one or more crowdsourcing platforms 36 through adapters 38, and also performs worker quality assessment 40 and answer aggregation 42; (2) a sampling strategy interface 44 with a unified user interface that enables a job requester to choose among various sampling strategies; (3) a job processing interface 46 that enables a job requester to launch jobs of various types (e.g., filter jobs 48, re-label jobs 50, and audit jobs 52); and (4) a data reporter dashboard 54 that shows the aggregated results from crowdsourcing and the improvement of the machine learning model 56.).
O’Neill, Kowolenko, Lasser and Wu are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill/Kowolenko/Lasser to combine the teaching of Wu, because curating can eliminate irrelevant or undesired data which leads to improved training data (Wu, [0028]).
Claims 31 and 38, are method and computer-readable storage medium claims that recite similar elements from claim 25, therefor the rationale applied in the rejection of claim 25 are also applicable.
Claims 26, 32 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), further in view of Lasser (US 20170351661), and furthermore in view of Pawar (US 20100332434).
Regarding Claim 26, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill/Kowolenko/Lasser do not appear to disclose comprising instructions to: receive an overall score for a definition among the plurality of definitions; and derive a weight for each individual guideline based on the overall score.
Pawar in the related art discloses: receive an overall score for a definition among the plurality of definitions; and derive a weight for each individual guideline based on the overall score. ([0031] An importance to an attribute 20-21 is typically translated into a weight assigned to that attribute. Traditionally equal weights have been assigned to attributes resulting in Static Weight Allocation; however, varying the weights (Dynamic Allocation of Weights) depending on the record to be classified, improves overall classification results. Thus for different records, attributes play a role in accordance with their importance (or weights).)
O’Neill, Kowolenko, Lasser and Pawar are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill/Kowolenko/Lasser to combine the teaching of Pawar, because the invention provides a tool that can be used to analyze data and classify new data records into different classifications according to the new records attribute weights and dynamically adjusted attribute weights of the previously collected data (Pawar, [0013]).
Claims 32 and 39, are method and computer-readable storage medium claims that recite similar elements from claim 26, therefor the rationale applied in the rejection of claim 26 are also applicable.
Claims 27, 33 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill (US 20120150848), in view of Kowolenko (US 20200409951), further in view of Lasser (US 20170351661), and furthermore in view of Bastide (US 20210263959).
Regarding Claim 27, O’Neill/Kowolenko/Lasser disclose all the elements of claim 21,
O’Neill/Kowolenko/Lasser do not appear to disclose comprising instructions to: based on a term among the plurality of terms, a corresponding definition associated with the term, and the set of labeled definitions, train the machine learning model comprising a neural network (NN) by feeding the part of speech feature input into the NN.
Bastide in the related art discloses: comprising instructions to: based on a term among the plurality of terms, a corresponding definition associated with the term, and the set of labeled definitions, train the machine learning model comprising a neural network (NN) by feeding the part of speech feature input into the NN. ([0034] The data storage module 152 may populate the program datastore 116 using natural language processing (NLP) such as, but not limited to, IBM® Watson natural language classifier (NLC), IBM® Watson natural language understanding (NLU), deep learning algorithms, and/or deep neural networks, e.g. deep convolutional neural networks. NLC models include multiple Support Vector Machines (SVMs) and a Convolutional Neural Network (CNNs). … the data storage module 152 may mark up a word in passages from the application data 134a, 134b, 134c to correspond to a particular part of speech. The data storage module 152 may read a passage or other text in natural language and assign a part of speech to each word or other token. The data storage module 152 may determine the part of speech to which a word (or other text element) corresponds based on the definition of the word and the context of the word.)
O’Neill, Kowolenko, Lasser and Bastide are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of O’Neill/Kowolenko/Lasser to combine the teaching of Bastide, because the content of the message may shed light on the meaning of texts elements in the related message (Bastide, [0034]).
Claims 33 and 40, are method and computer-readable storage medium claims that recite similar elements from claim 27, therefor the rationale applied in the rejection of claim 27 are also applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Vassilieva (US 20130246047) – discloses method/apparatus for extracting domain-specific acronyms and the associated definition, a classification model is used to select correct acronym definition pairs, and classification model apply user feedback to tune the classification model. See Abstract, and para 0009 and figs. 1, 3, and 5-6 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHILIP H LAM/ Examiner, Art Unit 2656
Prosecution Insights